Downscaling of radio brightness measurements for soil moisture estimation:A four-dimensional variational data assimilation approach

This paper investigates the feasibility of estimating large-scale soil mois ture profiles and related land surface variables from 1.4 GHz (L-band) pass ive microwave measurements, using variational data assimilation. Our four-d imensional assimilation algorithm takes into account both model and measure ment uncertainties and provides dynamically consistent interpolation and ex trapolation of remote sensing data over space and time. The land surface hy drologic model which forms the heart of the variational algorithm was expre ssly designed for data assimilation purposes. This model captures key physi cal processes while remaining computationally efficient. We test our algori thm with a series of synthetic experiments based on the Southern Great Plai ns 1997 Hydrology Experiment. These experiments provide insights about thre e issues that are crucial to the design of an operational soil moisture ass imilation system. Our first synthetic experiment shows that soil moisture c an be satisfactorily estimated at scales finer than the resolution of the b rightness images. This downscaling experiment indicates that brightness ima ges with a resolution of tens of kilometers can yield soil moisture profile estimates on a scale of a few kilometers, provided that micrometeorologica l, soil texture, and land cover inputs are available at the finer scale. In our second synthetic experiment we show that adequate soil moisture estima tes can be obtained even if quantitative precipitation data are not availab le. Model error terms estimated from radio brightness measurements are able to account in an aggregate way for the effects of precipitation events. In our third experiment we show that reductions in estimation performance res ulting from a decrease in the length of the assimilation time interval are offset by a substantial improvement in computational efficiency.